A multi-dimensional system exhibits behavior autonomously or as a function of multiple variables in response to a system input. A chaotic system is one that exhibits chaotic behavior (i.e., behavior characterized by random responses to an inexperienced observer) during normal operation. The brain is an example of a multi-dimensional system that also exhibits chaotic behavior during normal operation. However, in a relatively significant percentage of the human population, the brain experiences deterministic, abnormal episodes characterized by less chaotic behavior. This abnormal behavior may be caused by a wide variety of conditions. Epilepsy is one of these conditions.
Epilepsy is a chronic disorder characterized by recurrent brain dysfunction caused by paroxysmal electrical discharges in the cerebral cortex. At any given time, Epilepsy affects approximately 50 million people worldwide. If untreated, an individual afflicted with epilepsy is likely to experience repeated seizures, which typically involve some level of impaired consciousness. Some forms of epilepsy can be successfully treated through medical therapy. However, medical therapy is less effective with other forms of epilepsy, including Temporal Lobe Epilepsy (TLE) and Frontal Lobe Epilepsy (FLE). With TLE and FLE, removing the portion of the hippocampus and/or cerebral cortex responsible for initiating the paroxysmal electrical discharges, known as the epileptogenic focus, is sometimes performed in an effort to control the seizures.
For quite some time, the medical community has attempted to develop techniques that provide seizure prediction and/or seizure warning, where it will be understood that seizure prediction is a long-range forecast of seizure-onset time, whereas seizure warning is a long-range indication that conditions conducive to an impending seizure presently exist. Any such technique would certainly have numerous clinical and non-clinical application. For example, in order to more effectively treat certain Epilepsy patients, such a technique might be used in conjunction with a device, perhaps an implanted device, that delivers a dosage of anti-seizure medication into the patient's bloodstream for the purpose of averting an impending seizure.
In another example, such a technique might be used during a pre-surgical evaluation to assist in pinpointing the epileptogenic focus. It is understood that during a seizure, blood flow to the epileptogenic focus significantly increases. If certain radio-labeled ligands are injected into the patient's bloodstream in a timely manner, it is possible to monitor that increase using radiography, thereby allowing a surgeon to accurately pinpoint the boundaries of the epileptogenic focus, which the surgeon will remove during the surgery. A true seizure prediction and/or warning technique would provide an indication of an impending seizure well in advance, therefore providing sufficient time to prepare for and administer, for example, the aforementioned radiography ligand.
One of the most important tools for evaluating the physiological state of the brain is the electroencephalogram (EEG). The standard for analyzing and interpreting an EEG is visual inspection of the graphic tracing of the EEG by a trained clinical electroencephalographer. However, there is no established method for predicting seizure onset or for providing a seizure warning well before seizure onset by visually analyzing an EEG. Moreover, the use of traditional signal processing techniques on EEG signals has likewise yielded little practical information. These traditional techniques are limited in their effectiveness because the brain is a multi-dimensional system that produces nonlinear signals in space and time. Thus, traditional signal processing techniques employing standard, linear, time series analysis methods cannot detect the spatio-temporal properties that are critical in providing effective seizure warning and prediction.
U.S. Pat. No. 6,304,775, however, describes systems and methods capable of effectively generating true seizure warnings and predictions well in advance of impending seizures. The systems and methods described in this patent take advantage of the spatio-temporal characteristics exhibited by certain sites within the brain, when compared with the spatio-temporal characteristics exhibited by other sites within the brain, as these characteristics are noticeably different prior to an impending seizure as compared to the spatio-temporal characteristics exhibited by these same sites during seizure free intervals. In fact, these spatio-temporal characteristics may be noticeable hours, and in some cases, days before the occurrence of a seizure. As such, the systems and methods described in U.S. Pat. No. 6,304,775 use these differences as a seizure transition indicator.
More particularly, U.S. Pat. No. 6,304,775 describes, among other things, a technique that provides timely impending seizure warning (ISW), seizure susceptibility period detection (SSPD) and time to impending seizure prediction (TISP). The technique involves acquiring electrical or electromagnetic signals generated by the brain, where each signal corresponds to a single EEG electrode or channel. Each signal is pre-processed (e.g., amplified, filtered, digitized) and sampled. This results in a sequence of digital samples for each signal over a period of time, referred to therein as an epoch. The samples are then used to generate a phase-space portrait for each signal epoch.
For each phase-space portrait, a parameter reflecting rate of divergence is computed based on adjacent trajectories in the phase space, where rate of divergence, in turn, is one parameter that reflects the chaoticity level of the corresponding signal. In U.S. Pat. No. 6,304,775, the parameter used is the short-term, largest Lyapunov exponent (STLMAX).
In general, the STLMAX values associated with each EEG signal (i.e., each EEG channel) are compared to the STLMAX values associated with each of the other channels. The comparisons are preferably achieved by applying a T-statistic, which results in a sequence of statistical values, or T-index values, for each channel pair, where a sequence of T-index values represents a level of correlation or entrainment between the spatio-temporal response associated with the two channels that make up each channel pair.
The technique, when first employed, goes through an initialization period. During this initialization period, a number of “critical” channel pairs is identified, where U.S. Pat. No. 6,304,775 generally defines a critical channel pair as a pair of channels that exhibits a relatively high level of entrainment (i.e., relatively low T-index values for a predefined period of time) prior to seizure onset.
During the initialization period, a patient may experience one or more seizures. After each, the list of critical channel pairs is updated. Eventually, the list of critical channel pairs is considered sufficiently refined, and the initialization period is terminated. Thereafter, the ISW, SSPD and TISP functions may be activated and the T-index values associated with the critical channel pairs are monitored and employed in generating timely ISWs, SSPDs and/or TISPs.
Co-pending U.S. patent application Ser. No. 10/648,354 describes both methods and systems that optimize the critical channel selection process. Optimization is achieved in several ways. First, the selection is achieved more efficiently as it is based on a limited amount of statistical data (e.g., T-index data) within a pre-defined time window preceding, and in some instances, following seizure-related events. Critical channel selection is further optimized by selecting and reselecting critical channels for each of a number of predictors, where a predictor is a given number of critical channel groups “x”, a given number of channels per group “y”, and a given total number of channels “N.”